Thinned-ECOC ensemble based on sequential code shrinking

Error-correcting output coding (ECOC) is a strategy to create classifier ensembles which reduces a multi-class problem into some binary sub-problems. A key issue in designing any ECOC classifier refers to defining optimal codematrix having maximum discrimination power and minimum number of columns. This paper proposes a heuristic method for application-dependent design of optimal ECOC matrix based on a thinning algorithm. The main idea of the proposed Thinned-ECOC method is to successively remove some redundant and unnecessary columns of any initial codematrix based on a metric defined for each column. As a result, computational cost of the ensemble is reduced while preserving its accuracy. Proposed method has been validated using the UCI machine learning database and further applied to a couple of real-world pattern recognition problems (the face recognition and gene expression based cancer classification). Experimental results emphasize the robustness of Thinned-ECOC in comparison with existing state-of-the-art code generation methods.     

A probabilistic integrated object recognition and tracking framework

This paper describes a probabilistic integrated object recognition and tracking framework called PIORT, together with two specific methods derived from it, which are evaluated experimentally in several test video sequences. The first step in the proposed framework is a static recognition module that provides class probabilities for each pixel of the image from a set of local features. These probabilities are updated dynamically and supplied to a tracking decision module capable of handling full and partial occlusions. The two specific methods presented use RGB color features and differ in the classifier implemented: one is a Bayesian method based on maximum likelihood and the other one is based on a neural network. The experimental results obtained have shown that, on one hand, the neural net based approach performs similarly and sometimes better than the Bayesian approach when they are integrated within the tracking framework. And on the other hand, our PIORT methods have achieved better results when compared to other published tracking methods in video sequences taken with a moving camera and including full and partial occlusions of the tracked object.     

Display text segmentation after learning best-fitted OCR binarization parameters

In this paper text segmentation in generic displays is proposed through learning the best binarization values for a commercial optical character recognition (OCR) system. The commercial OCR is briefly introduced as well as the parameters that affect the binarization for improving the classification scores. The purpose of this work is to provide the capability to automatically evaluate standard textual display information, so that tasks that involve visual user verification can be performed without human intervention. The problem to be solved is to recognize text characters that appear on the display, as well as the color of the characters’ foreground and background. The paper introduces how the thresholds are learnt through: (a) selecting lightness or hue component of a color input cell, (b) enhancing the bitmaps’ quality, and (c) calculating the segmentation threshold range for this cell. Then, starting from the threshold ranges learnt at each display cell, the best threshold for each cell is gotten. The input and output data sets for testing the algorithms proposed are described, as well as the analysis of the results obtained.     

Applications of thermal image and extension theory to biometric personal recognition

In this paper, a new scheme is proposed to design a biometric personal recognition system. First, this paper used the thermal image of the hand by using infrared camera to build the sensor module of the recognition system; the extraction features include the length of palmar midpoint to each finger, palmar profile, finger length and finger width. The thermal image presented in this paper was detects infrared energy and converts it into an electronic signal. Then a new recognition method based on the extension is proposed to perform the core of the personal recognition system. The experimental results confirmed that proposed recognition system has a very high recognition rates, therefore, this paper verification using thermal image of the hand to identity recognition was feasible.     

汽车ABS模糊参数自整定PID控制方法的仿真研究

防抱死制动系统(ABS)是汽车安全系统的重要组成部分,对行驶路面状况进行实时准确的自动识别和提高ABS控制算法的鲁棒性具有重大意义。通过仿真分析,提出了一种简单有效的路面识别算法,并设计了以最优滑移率为控制目标的模糊PID控制器。结合车辆模型,对该系统在变附着系数路面的运行情况进行了仿真。结果表明,该系统能够及时判断出路面状况的变化,自动调节控制器参数,使车辆获得最大地面制动力。     

CAPTCHA技术研究综述

全自动开放式人机区分图灵测试(completely automated public Turing test to tell computers and humans apart,CAPTCHA)又称为人机交互验证(human interactive proof,HIP),它能自动产生并评估一个测试,这个测试能被几乎所有人类用户通过,而现有的计算机程序不能通过.CAPTCHA提供了一种自动驱分人和机器的手段,已成为一种标准的网络安全技术成功应用于包括Google,Yahoo!以及微软在内的各大网站.CAPTCHA设计基于人工智能领域的开放性问题,按表现载体和内容不同分为文本、图像、声音3种类型,其中,基于字符识别的文本CAPTCHA已得到广泛使用;图像CAPTCHA利用计算机视觉中的难解问题,目前尚处于研究阶段;声音CAPTCHA针对视觉残障者,是对前两种视觉CAPTCHA的补充.介绍了CAPTCHA的发展和设计准则,详细阐述CAPTCHA设计和破解的研究工作及最新进展,给出典型实例,讨论其可用性和安全性,最后指出未来CAPTCHA技术的发展方向和亟待解决的问题.     

改进OMP算法在人脸识别中的应用

分析稀疏表示的人脸识别方法的基本原理,针对采用基于正交匹配追踪(OMP)的稀疏表示算法时,所获得稀疏系数存在负值的问题,提出一种改进的正交匹配追踪算法。通过对稀疏系数的大小进行直接约束,减少负值稀疏系数的产生及算法迭代次数,并提高人脸识别速度。在ORL人脸数据库中的实验结果证明,改进后算法的识别率比原有算法提高了3%,迭代次数设置为7次最为合理。     

基于深度图像技术的手势识别方法

针对复杂环境下的手势识别问题,提出一种基于深度图像技术的手势识别方法。利用深度图像信息从复杂环境中提取手势区域,综合手势的表观特征,建立决策树实现手势的识别。对常见的9种手势在复杂背景条件下进行测试,实验结果表明,手势的平均识别率可达到98.4%,速度达到每秒25帧。     

基于关键词抽取的自动文摘算法?

针对生成文摘内容不完整的问题,利用相邻词的共现频率进行未登录词识别,提出一种通过词汇链的构建进行中文关键词抽取和文摘生成的算法,并给出一种采用《知网》为知识库构建词汇链的方法。通过计算词义相似度构建词汇链,结合词汇所在词汇链的强度、信息熵和出现位置等属性,进行关键词抽取和句子重要度计算。实验结果表明,与已有算法相比,该算法能够提高生成摘要的召回率和准确率。     

一种软件系统需求风险评估方法

提出一种软件系统需求风险评估方法。通过构造Bayesian网络分析需求风险以及风险源之间的因果关系,利用条件概率表定量描述需求风险发生概率,应用层次分析法进行风险影响的评估,得到各项需求风险的综合风险值。仿真结果表明,该方法能较准确地评估软件需求分析阶段的风险等级,在等级划分方面列出了一些主要的需求风险,从而有助于开展风险管理,增强软件的可靠性。